Neural network-based topology optimization for microstructures with negative Poisson ratio
WANG Yi
GUO Yanding
GU Guancheng
GANG Tieqiang
CHEN Lijie
Abstract：[Objective]To address the high computational cost of sensitivity analysis in microstructure topology optimization,this study aims to develop an efficient design framework based on neural networks for microstructures with negative Poisson ratio.[Methods]The fully-connected feedforward neural network(FFNN)model was established,mapping the coordinates of the design domain to the density field.The back-propagation algorithm was utilized for sensitivity analysis to reduce computational redundancy.Numerical simulations and tensile tests were conducted on the optimized high-resolution negative Poisson ratio microstructures to verify the proposed method.[Results]The results indicate that the FFNN model framework effectively improves the efficiency of sensitivity analysis.The optimized microstructures exhibit significant negative Poisson ratio effects,and the consistency between simulation and test results demonstrates the validity and robustness of the optimization approach,providing new insights for the design of complex microstructures.
Keywords：Neural networkTopology optimizationNegative Poisson ratio microstructureSensitivity analysisFinite element method
Publication Date：2026-03-15
Online Publishing Date：2026-09-12(First online date of this platform, not the publication date of the document)
Pages：8( 96-103 )